# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
rocfile='C:/Users/DELL/Desktop/huawei/FaceBoxes-GPU/DiscROC.txt'
roc = pd.read_csv(rocfile, sep=' ', header=None)
roc.columns = ['tpr', 'fp', 'threshold']
def plot_roc():
    _, axis = plt.subplots(nrows=1, ncols=1, figsize=(7, 4), dpi=120)
    axis.plot(roc.fp, roc.tpr, c='r', linewidth=2.0);
    axis.set_title('Discrete Score ROC')
    axis.set_xlim([0, 2000.0])
    axis.set_ylim([0.6, 1.0])
    axis.set_xlabel('False Positives')
    axis.set_ylabel('True Positive Rate');
    plt.show()

plot_roc()